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Record W3087410264 · doi:10.1123/jmpb.2019-0050

Physical Activity Tracking Wristbands for Use in Research With Older Adults: An Overview and Recommendations

2020· article· en· W3087410264 on OpenAlexaff
Alanna Weisberg, Alexandre Monte Campelo, Tanzeel Bhaidani, Larry Katz

Bibliographic record

VenueJournal for the Measurement of Physical Behaviour · 2020
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTracking (education)Activity trackerObservational studyWearable computerPhysical activityIntervention (counseling)PopulationPsychologyApplied psychologyWearable technologyComputer sciencePhysical medicine and rehabilitationMedicine

Abstract

fetched live from OpenAlex

Traditional physical activity tracking tools, such as self-report questionnaires, are inherently subjective and vulnerable to bias. Physical activity tracking technology, such as activity tracking wristbands, is becoming more reliable and readily available. As such, researchers are employing these objective measurement tools in both observational- and intervention-based studies. There remains a gap in the literature on how to properly select activity tracking wristbands for research, specifically for the older adult population. This paper outlines considerations for choosing the most appropriate wrist-worn wearable device for use in research with older adults. Device features, outcome measures, population, and methodological considerations are explored.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.734
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.545
GPT teacher head0.490
Teacher spread0.055 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2020
Admission routes1
Has abstractyes

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